Device energy management

Abstract

A method comprising: obtaining details of a plurality of devices (e.g. computers, smart phones, refrigerators, ovens, washing machines) operating within a local network, wherein the details include de

Description

Field of the Invention The present invention relates to a system and method for managing electrical energy or devices within a network, and especially monitoring energy usage of Wi-Fi (RTM) connected devices. 10 Background of the Invention 15 All electrical devices consume power, but it can be difficult to determine how much power each device is consuming. Furthermore, devices do not always consume power at the same rate. Whilst devices such as refrigerators can be powered more or less continuously, televisions may have high energy consumption at specific times of the day and low or no energy consumption at other times. An energy provider may only be aware of the overall energy consumption of a property by information gathered by an electric meter. Even if a smart meter is installed and communicating regularly with the energy provider, this may only provide the overall energy consumption of a property at different times. There is no straightforward way to determine a breakdown of energy consumption for individual devices without additional hardware. If such a breakdown of energy consumption per device is not available then it is not possible to manage or limit energy consumption effectively. This can lead to inefficiencies, 25 which can be significant, especially amongst large numbers of properties. Without such efficiency gains, the environmental impact of electricity generation may be increased unnecessarily. Furthermore, the ability to more effectively manage the energy consumption of devices within a property can mean that energy savings can be realised without significantly affecting the functionality of individual devices. 30 One option for determining the energy consumption of individual devices is by using smart plugs to measure electrical power for individual devices directly. However, the smart plugs will also consume power. It is also impractical to install smart plugs for every device in a property. Furthermore, individual smart plugs cannot effectively manage multiple 35 devices at the same time. Therefore, there is required a method and system that overcomes these problems. Summary of the Invention 10 CXI 15 A local network (e.g., within a property such as a home or office), such as a Wi-Fi (RTM) network served by a broadband router, contains a plurality of connected devices. Each connected device will have its own electrical energy consumption. The system determines the energy consumption of each device. This may be determined in terms of an instantaneous power (W) or the energy used over a time period (kWh). These data may be provided by each device directly. For example, an energy sensor or current meter within the device may provide the data as an output signal. If a device does not contain an energy sensor, then its power consumption may be reported by another device or in another way. For example, this may be a smart plug that measures the power provided to the mains supply of the device (e.g., 110v or 240v). Within the local network there may be a mix of directly reporting devices and indirectly reporting devices. Preferably, the system and method collect the energy consumption data from each device in the same data format. These data may be collected over Wi-Fi (RTM) or another local wireless protocol (e.g., Bluetooth (RTM), BLE, Zigbee (RTM), Z-wave, Matter, etc.). 20 25 However, the energy consumption data are determined, the system and method generate an overall view the energy consumption of the devices within the local network. For example, this view or output may order the devices according to energy consumption or average energy consumption. In an example implementation, device types may be grouped together. A consolidated data set or distribution of overall energy consumption may be provided, for example on a device display (e.g., a smart phone) or as an output message or data. 30 The system and method may further analyse the consolidated data set of energy consumption for the devices in the local network and make recommendations to a user based on this analysis. Automated actions may be triggered based on the output. For example, the system may communicate with individual devices to instruct them to make a configuration change. This may include changing a power mode (e.g., from “on” to “standby”) or switch off the device. The system and method may provide the output to external entities. This may take the form of a digital fingerprint or digital shadow based on the energy consumption of the local network (e.g., property). External entities may be provided access to the data of the digital fingerprint or digital shadow for the local network or a plurality of local networks. This access may be provided as an application programming interface (API) response following an API call from the external entity. Against this background and in accordance with a first aspect there is provided a method for managing devices in a local network, the method comprising the steps of: obtaining details of a plurality of devices operating within the local network, wherein the details include device identifiers; determining electricity usage for each device in the plurality of devices operating within the local network; and generating a consolidated data set of electricity usage for the plurality of devices operating within the local network. Therefore, the energy usage of a property served by the local network can be monitored and managed more effectively. Optionally, the step of determining electricity usage for each device in the plurality of devices may further comprise the step of receiving from each device data providing the electricity usage. Each device may monitor its own energy consumption (e.g., current and / or power over time) and report this in a suitable format (e.g., a standardised format). This may require software and sensors within each device to carry out these measurements. This may be a direct way to determine electricity usage. Optionally, the step of determining electricity usage for each device in the plurality of devices further comprises the steps of: determining from the obtained details a device type for each device operating within the local network; and retrieving from a data storage, data indicating electricity usage for the device type of each device. This may be an indirect method but can be effective for certain types of devices that have a predictable energy consumption. For example, refrigerators and freezers may be operating all of the time and have substantially constant and regular electricity usage (e.g., provided by a manufacturer or external tester). Such data can be stored in a database or other datastore and associated with identifiers of different devices. Optionally, the data indicating the electricity usage may include electricity usage during a low power mode and a high power mode of at least one device type. Therefore, provided information that indicates the device type and what power mode it was operating in (e.g., low, medium, high, sleep, standby, full operation, etc.) and provided that each 5 mode is associated with a predetermined, or stored electricity or energy consumption parameter, then the actual electricity usage can be estimated relatively precisely without requiring direct measurements or sensors within each device. Optionally, the step of determining electricity usage for each device in the plurality 10 of devices operating within the local network may further comprise determining an amount of time of the at least one device spent in the low power mode and an amount of time spent in the high power mode. Therefore, a more accurate estimation of the electricity usage for each device can be made without requiring sensors or monitoring software within the device. LO 15 Preferably, the local network may be a Wi-Fi (RTM) network. The local network CO may include or be based on other wireless or wired protocols. This may include Ethernet or powerline communications, for example. 1 20 The step of determining electricity usage for each device in the plurality of devices operating within the local network further comprises: determining a Wi-Fi (RTM) signal strength of at least one of the plurality of devices operating within the local network. Devices may reduce their Wi-Fi (RTM) signal strength when in a sleep or low-power mode. Furthermore, devices may disable or switch off 25 certain Wi-Fi (RTM) bands if they have more than one band or frequency. A wireless router may be aware of the capabilities of each device and so can determine when such changes arise. This can indicate a power mode or electricity usage of each device without requiring on-device sensors or monitoring or connected smart plugs to directly measure electrical power. When the router or other component detects that a device has switched 30 off one or more Wi-Fi (RTM) bands then a determination may be made that the device is in a low power or standby mode, for example. When the router or other component detects that a device has switched on all Wi-Fi (RTM) bands then a determination may be made that the device is in a high power or fully operational mode, for example. Current digital fingerprinting techniques may include appropriate Wi-Fi (RTM) signal strength, but a further 35 enhancement is the use of these data to infer energy usage of the device. Optionally, the consolidated data set of electricity usage may be a relative ranking of electricity usage. The consolidated data can be in other formats such as listed in order of average electricity usage or by historical power usage by day, week, month, year, etc. Optionally, the method may further comprise the step of: taking an action based on the consolidated data set of electricity usage. Therefore, as well as monitoring the electricity usage of a property served by the local network, actions can be taken to reduce electricity usage and improve efficiency without compromising functionality. Optionally, the action may be switching one or more of the devices of the plurality of devices from a high power mode to a low power mode or switching off one or more of the devices. Other actions may be taken. Optionally, the method may further comprise the step of providing the consolidated data set of electricity usage for the plurality of devices operating within the local network to a server external to the local network. Therefore, different entities and organisations can make use of the information generated by one or more local networks. Optionally, the step of providing the consolidated data set of electricity usage for the plurality of devices operating within the local network to a server external to the local network may be an application programming interface, API, response carried out in response to an API request or call by the server or another entity external to the local network. Some details within the information provided by the API, including localisation information, may vary for different local networks and depending on whether the user or customer has provided consent. The returned data may be adjusted based on privacy wishes, for example. Optionally, the provided data may include location data of the local network. This may be provided by some but not all of the local networks, depending on privacy preferences by users or customers administering the local networks. Optionally, the method may further comprise the step of: taking an action at the server based on the consolidated data set of electricity usage. Other actions taken at the server or external to the local network may include changing how electrical power is supplied to a property served by the local network. For example, different tariffs can be used to charge for electricity. In an example implementation, a tariff may alter costs for electricity at different times of day. The actions may include switching on or off devices at different times of day and synchronised with the tariff to make use of low cost electricity at different times. Optionally, the plurality of devices may include any one or more of: television; computer; refrigerator; freezer; smart phone; camera; washing machine; a dryer; a water heater; dish washer; and / or oven. Other devices may be included. Optionally, the details of the plurality of devices operating within the local network may comprise any one or more of: device name; device type; location; IP address; MAC address; and brand. Other details may be included. Preferably, the step of obtaining details of the plurality of devices operating within the local network, may comprise receiving the details in a standardised format. The standardised format may be an existing format used to obtain details of different network device. Optionally, the standardised format may be any one of: Matter; Broadband Forum (BBF); and comma separated variable (CSV). Other formats may be used. 5 In accordance with a second aspect, there is provided a system comprising means for carrying out any of the methods described above. Optionally, the means for carrying out the method may be within a broadband router. The broadband router may include functionality or components to provide one or 10 more wireless protocols such as Wi-Fi (RTM), Bluetooth (RTM), BLE, Zigbee (RTM), Z-wave, etc. The methods described above may be implemented as a computer program comprising program instructions to operate a computer. The computer program may be LO 15 stored on a computer-readable medium, including a non-transitory computer-readable medium. CO The computer system may include a processor or processors (e.g., local, virtual or cloud-based) such as a Central Processing Unit (CPU), and / or a single or a collection of 1 20 Graphics Processing Units (GPUs). The processor may execute logic in the form of a software program. The computer system may include a memory including volatile and nonvolatile storage medium. A computer-readable medium (CRM) may be included to store the logic or program instructions. For example, embodiments may include a non-transitory computer-readable medium (CRM) storing software comprising instructions executable by 25 one or more computers which, upon such execution, cause the one or more computers to perform the disclosed methods. Non-transitory CRM may refer to a CRM that stores data for short periods or in the presence of power such as a memory device or Random Access Memory (RAM). For example, a non-transitory computer-readable medium may include storage components, such as, a hard disk (e.g., a magnetic disk, an optical disk, a 30 magneto-optic disk, and / or a solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, and / or a magnetic tape. The different parts of the system may be connected using a network (e.g. wireless networks and wired networks). The computer system may include one or more interfaces. The computer system may contain a suitable operating system such as UNIX, Windows (RTM) or Linux, for example. It should be noted that any feature described above may be used with any particular aspect or embodiment of the invention. Brief description of the Figures The present invention may be put into practice in a number of ways and embodiments will now be described by way of example only and with reference to the accompanying drawings, in which: FIG. 1 a flowchart of a method for managing devices in a local network, such as a home or office environment; FIG. 2 shows a schematic diagram of a computer system used to implement the method of Figure 1; FIG. 3 shows an example data model format used in the method of Figure 1; FIG. 4 shows an example consolidated data set format generated by the method of Figure 1, according to first user preferences; FIG. 5 shows an example consolidated data set format generated by the method of Figure 1, according to second user preferences; FIG. 6 shows a schematic diagram illustrating how a third party application provider makes an API call request and receives a response as part of the method of Figure 1; FIG. 7 illustrates schematically device fingerprinting in which data is obtained and inferred about devices in the local network; FIG. 8 shows screens shots of a mobile application used to display information obtained using the method of Figure 1; FIG. 9 shows schematically the use of different data models to update data between devices in the local network and virtual representations of those data; FIG. 10 shows schematically the updating of data between devices in the local network and virtual representations of those data; FIG. 11 shows further screens shots of the mobile application used to display information obtained using the method of Figure 1; FIG. 12 shows further screens shots of the mobile application used to display information obtained using the method of Figure 1; FIG. 13 shows further screens shots of the mobile application used to display information obtained using the method of Figure 1; and FIG. 14 shows further screens shots of the mobile application used to display information obtained using the method of Figure 1. It should be noted that the figures are illustrated for simplicity and are not necessarily drawn to scale. Like features are provided with the same reference numerals. Detailed description of the preferred embodiments 5 Figure 1 shows a flowchart of a method 100 for managing devices in a local network, such as a Wi-Fi (RTM) network served by a broadband router. The local network may be situated within a home or office environment, for example. Device details or properties are obtained at step 110. The device details may include different parameters 10 such as device identifier, device type, manufacturer and many other fields. Each device may have different details. At step 120, electrical usage for each device is determined. There may be different ways of determining, measuring and / or recording electrical or energy usage of each device. For example, the device details obtained at step 110 may include one or more values indicating electricity usage of the device. In another example 15 implementation, energy usage may be determined indirectly. For example, energy usage of a device may be determined by searching for an estimate of electricity usage from a database or data store for the particular device type. If the device can have different modes of operation and each mode may have a different energy usage, then the electricity usage of the device can be estimated based on amounts of time spent in different modes of 20 operation. Each device may report how long they have been in each mode of operation or this may be inferred from other activity. For example, Wi-Fi (RTM) signal strength or the number of Wi-Fi (RTM) bands in operation by a device may indicate a low or high power mode. Obtaining this information at regular intervals can allow the system and method to determine or estimate how long each device has been operating in each power mode. 25 The electrical usage of a device may be inferred from its Wi-Fi (RTM) parameters or signal. For example, a device may be able to operate on different Wi-Fi (RTM) bands (e.g., a dual-band device operating at 2.4GHz and 5GHz). For dual-band devices, if it is determined that only 2.4GHz is being used then it may be inferred that the device is 30 operating a lower power mode. Furthermore, the specific Wi-Fi (RTM) protocol may be used to infer a power mode and so electricity usage. In any case, at step 130 a consolidated data set of electricity usage is generated for the devices in the local network. The format and content of the consolidated data can take 35 different forms. However, the electricity usage of all devices in the local network (or at least a subset of all devices) is generated. Therefore, the electricity usage of different devices can be compared, ranked, or otherwise interpreted. This may be automatic, with the results forming an output, or manually presented to a user. The consolidated data set may be described as an energy fingerprint of the local network or property served by the 5 local network (e.g., a home or office). Other device data may be included in the consolidated data set (e.g., identifiers, device type, location data, etc.). At step 140, an action may be taken based on the consolidated data set. For example, the device or devices with the highest electricity usage (or those reaching a 10 threshold) may be identified. Actions may be taken on this subset (or single) device. For example, the user may be prompted or reminded to take an action, such as to turn off the device. The action may be automated. For example, the device may be switched off or placed into a lower power mode of operation (e.g., a standby mode). Such commands may be transmitted over Wi-Fi (RTM) or another communication protocol. The method 100 may LO 15 be implemented as part of a computer system 200. CM CO As shown in Figure 2, the computer system 200 includes a number of components including communication interfaces 220, system circuitry 230, input / output (I / O) circuitry 240, display circuitry and interfaces 250, and a datastore 270. The system circuitry 220 1 20 can include one or more processors or CPUs 280 and memory 290. The system circuitry 230 may include any combination of hardware, software, firmware, and / or other circuitry. The system circuitry 230 may be implemented, with one or more systems on a chip (SoC), application specific integrated circuits (ASIC), microprocessors, and / or analog and digital circuits. 25 The display circuitry may provide one or more graphical user interfaces (GUIs) 260 and the I / O interface circuitry 240 may include touch sensitive or non-touch displays, sound, voice or other recognition inputs, buttons, switches, speakers, sounders, and other user interface elements. The I / O interface circuitry 240 may include microphones, 30 cameras, headset and microphone input / output connectors, Universal Serial Bus (USB) connectors, and SD or other memory card sockets. The I / O interface circuitry 240 may further include data media interfaces (e.g., a CD-ROM or DVD drive) and other bus and display interfaces. The memory 290 may include volatile (RAM) or non-volatile memory (e.g., ROM or Flash memory). The memory may store the operating system 292 of the computer system 200, applications or software 294, dynamic data 296, and / or static data 298. The datastore or data source 270 may include one or more databases 272, 274 and / or a file store or file system, for example. Figure 3 shows an example data format for an application programming interface (API) return following an API call. The API may be part of a network as a platform (NaaP) service, for example. The API provides the consolidated dataset of electricity usage for the local network. The system and method augments device data models to provide energy parameters, enabling communication using different protocols including a new NaaP API. The energy fingerprint or an energy digital shadow of a property served by the local network may be stored in a physical or virtual storage location (e.g., cloud storage). The API may be exposed to third parties (e.g., suitably anonymised, unless the user or customer provides consent to data sharing). In an example implementation, energy companies may obtain services from broadband or internet service providers implementing the system and method to generate data so that the energy companies can segment their customer base into more granular segments or personas. That may enable them to share “cost per device per day” information and / or offer more customised tariffs, based on energy requirements of devices in each property. The API may be included in standard services or added to TeleManagement Forum (TM Forum) open APIs. This can be used in exposing life cycle management of network services for both design time declaration and run-time execution. Quotes, orders, and diagnostics capability can be exposed to users. Product catalog services can be exposed internally and externally. Customer data can be exposed internally. In the example format of Figure 3, N represents the number of devices or connected devices. The next part of the data format describes a data model type (e.g., a data model reference) and the number of parameters forming the data model. The final section of the data format shown in Figure 3 is the payload or parameter values for each device in the local network. Therefore, the combination of the number of connected devices, data model type reference (e.g., Matter standard reference) and / or number of parameters in the data model enables the API response to be processed correctly, with the number of following parameters used as the payload of the response to an API call (request). A timestamp may be included to indicate the date and / or time when the energy measurement or measurements were made. For example, the measurements may be made either in response to the API request or at an earlier time. There may be scheduled (e.g., daily) measurements and the API call retrieves these from a database, for example. A geo-location tag may also be included, which could for example enable an energy utility customer of the API to correlate energy consumption with local weather abd.ir temperature conditions. Certain data, such as geo-location information, may be excluded from the returned data if the user has not provided permission to share such information. Figures 4 and 5 show example data models depending on whether a customer has opted in to enable full data sharing (e.g., including identifying and geo-location information). The exact parameters to be included or permissible in an opt-out scenario will depend on local privacy or data law regulations. Figure 4 shows an example opt-in data model. The data included in this model augments device fingerprinting parameters with additional device energy use parameters. Preferably, the data model can be aligned or compatible with a standardised data model, for example from Matter, broadband forum, (BBF), etc. Customer reference or identifiers may be included the API call. Any additional geo-location information can also be specific in this scenario. Figure 5 shows an example opt-out data model. In this case, some specific device fingerprinting parameters are omitted. The example data model only includes basic device type and energy use parameters. Any customer home reference in the API can be random or otherwise anonymised. Any additional geo-location information may be made less precise or vague (e.g., only contain county or region). A NaaP Network Exposure Layer (NEL) may process the gathered device data so that the northbound data model used to communicate the home’s device energy information via the NaaP API can align with the customer’s opt-in opt-out preferences. Figure 6 shows a schematic diagram illustrating how a third party application provider 600 makes the API call or request and receives a response (e.g., from a network component of an internet service provider (ISP). This may be achieved using a network platform abstraction 610. A published API 620 facilitates the request and response in the form of API calls. The network platform abstraction 610 obtains data from a cloud storage or other storage location 630. The API may be provided by the network as a platform (NaaP) system, for example. This system and method allow third parties, such as energy providers to more easily and efficiently obtain device energy information from each property. The approach may be independent of the home device management protocol and Message Transfer Protocol (e.g., TR-069 CWMP, TR-369 USP, SNMP, etc.). In a network management approach, granularity (number of energy consumption data points used to construct the data model) can be evolved over time by the network operator independent of the API. Therefore, NaaP APIs can abstract the detail and complexity away from the API customer (e.g., energy utility or application developer). Figure 7 illustrates schematically the concept of device fingerprinting. In this example, the device is a smart phone but can be any device, especially those with a mains electricity source. The customer premises equipment (CPE) such as a broadband router can directly receive some data from the connected device. However, certain further data can be inferred, as indicated by the data set “Device Fingerprinting”. Device fingerprinting is a technology that enriches device information based on device database intelligence. The present system and method expands this concept to include electricity and energy fingerprinting with additional device information being provided by the device itself and / or by looking up stored energy information from external data sources. Figure 8 shows screenshots of a mobile device application that provides device fingerprinting information. Screen 1 shows the identification of devices on the local network and screen 2 shows more details about an individual device. Figure 9 shows a schematic diagram of alternative data models for storing the device energy information. Arrows 900 indicate manual data flows and arrows 910 indicate automatic data flow. Figure 9A shows a data model in which manual data flows are used to capture information from the physical devices 920 and store them in a digital or virtual environment 930. Figure 9B illustrates schematically, automated data flows from the physical devices 920 to the virtual environment 930 (i.e., using the methods described above) but with manual updates or configuration changes made from the virtual environment 930 back to the physical devices 920. This can be defined as a digital shadow data model. Figure 9C shows data flowing automatically in both directions. This can be defined as a digital twin data model. Therefore, automated actions can be taken and implemented in the real world as device energy information is received and changes. 10 CXI 15 Figure 10 shows schematically and in more detail the data flowing between the real world devices and the virtual environment. As described previously, device fingerprinting creates an energy fingerprint of devices within the local network. The simple data model has been expanded from device identification to include its energy consumption characteristics obtained using the described methods. For example, in a first option, each device type can be identified (e.g., manufacturer, device type, model, etc.). A look-up table may be used to provide the maximum and typical power consumption in different modes of operation (e.g., active and standby modes) for each device. In an alternative implementation, each (or any) device can measure its device energy consumption and provide this information through the local network to a virtual server or external entity, e.g., in a cloud storage (like a virtual meter). Information from each device may be acquired over a local wireless network such as Wi-Fi (RTM), Z-wave, Zigbee (RTM), Matter, etc. preferably in a predetermined format with agreed parameters in using a data model with telemetry options. The BBF TR-181 data model includes some options for this. This could be a standardised data model so that different device suppliers can align and integrate more effectively. 20 25 30 In an example implementation using a digital shadow data model, once the energy fingerprint of a home is captured (measured or identified and then modelled), the information can be used to construct a dashboard. The dashboard may show the home or business owner (or any other user) what devices are active and the times of this activity. This dashboard may also illustrate and present which devices consume the most energy. This can enable opportunities or recommendations to the user suggesting which devices could be switched off overnight or at other times to reduce energy costs. In a further example implementation, a closed loop energy saving or management system may be used. This can go beyond a dashboard scenario with recommendations to the user or customer being replaced or supplemented to automatically take actions, such as switching off certain devices or putting them into a sleep mode automatically based on the information received from the local network. Such decisions may be threshold-based or machine learning (ML) or Al could characterise a customer’s home, learn what devices are used and when, which devices consume the most energy, and propose or implement 10 CXI 15 20 an optimal approach to energy reduction without impacting the customers’ (domestic or commercial) normal activities and associated device usage routines. Therefore, this enhanced approach adds power management commands to device data models. Further energy saving features may extend beyond turning devices on or off, or putting them into sleep mode. Other device operating parameters may be adjusted to optimise power usage. For example, Wi-Fi (RTM) RF capacity can be managed more effectively. Wi-Fi (RTM) RF capacity may be right-sized in the home or network environment based on demand. Wi-Fi (RTM) 7 allows three Wi-Fi (RTM) bands (2.4, 5, and 6GHz) to be used in aggregate. This pool of Wi-Fi (RTM) capacity offers a capacity of >30Gbps. However, such a large or maximum Wi-Fi (RTM) capacity may not be required at all times. For example, during the night only basic telemetry security cameras together with occasional software updates may be required in the local network. ML may be used to understand the daily diurnal and weekly Wi-Fi (RTM) usage in the home and calculate what Wi-Fi (RTM) capacity and / or coverage is needed to deliver unfettered operation. For example, the system and method may monitor Wi-Fi (RTM) usage, volumes, traffic, and numbers of actively connected devices. When Wi-Fi (RTM) demand is measured as low or below certain predetermined thresholds, it is possible to turn off one or two of the three WiFi (RTM) bands. This further reduces power as the transceiver no longer needs to flood the home or office environment with RF that is not required at that instance (saving around 3-4W per band). Such an action may be performed manually or automatically based on the detected results for a particular local network (e.g., using the ability to expose the aggregated measurement data of the NaaP API). Furthermore, wake on LAN may be used with internet of things (loT) and other low-25 powered or low complexity devices and not just be limited to PCs, laptops and tablets computers. User services platform (USP) control may be extended to smart plugs and power strips. Figure 11 shows an example screen shot of a display of a consolidated data set of 30 electricity usage for the plurality of devices operating within the local network. In this example, different colours or shading may be used to indicate, the relative consumption of electricity for each device relative to each other or an average. This may be based on the average usage for that device type or for all devices in the local network. LO CXI Figure 12 shows a further example screen shot of a display of a consolidated data set of electricity usage for the plurality of devices operating within the local network. This figure illustrates how a first screen can show an overview of a plurality (or all) devices in the 14 03 25

Claims

1. A method for managing devices in a local network, the method comprising the steps of:5 obtaining details of a plurality of devices operating within the local network, whereinthe details include device identifiers;determining electricity usage for each device in the plurality of devices operating within the local network; andgenerating a consolidated data set of electricity usage for the plurality of devices10 operating within the local network,wherein the step of determining electricity usage for each device in the plurality of devices operating within the local network further comprises:determining a Wi-Fi (RTM) signal strength of at least one of the plurality of devices operating within the local network.

152. The method of claim 1, wherein the step of determining electricity usage for each device in the plurality of devices further comprises the step of receiving from each device data providing the electricity usage.20 3. The method of claim 1, wherein the step of determining electricity usage for eachdevice in the plurality of devices further comprises the steps of:determining from the obtained details a device type for each device operating within the local network; andretrieving from a data storage, data indicating electricity usage for the device type of 25 each device.

4. The method of claim 3, wherein the data indicating the electricity usage includes electricity usage during a low power mode and a high power mode of at least one device type.

305. The method of claim 4, wherein the step of determining electricity usage for each device in the plurality of devices operating within the local network further comprises determining an amount of time of the at least one device spent in the low power mode and an amount of time spent in the high power mode.14 03 256. The method according to any previous claim, wherein the local network is a Wi-Fi (RTM) network.

7. The method according to any previous claim, wherein the consolidated data set of5 electricity usage is a relative ranking of electricity usage.

8. The method according to any previous claim further comprising the step of: taking an action based on the consolidated data set of electricity usage.10 9. The method of claim 8, wherein the action is switching one or more of the devices ofthe plurality of devices from a high power mode to a low power mode or switching off one or more of the devices.

10. The method according to any previous claim further comprising the step of providing15 the consolidated data set of electricity usage for the plurality of devices operating within the local network to a server external to the local network.

11. The method of claim 10, wherein the step of providing the consolidated data set of electricity usage for the plurality of devices operating within the local network to a server20 external to the local network is an application programming interface, API, response carried out in response to an API request by the server external to the local network.

12. The method of claim 10 or claim 11, wherein the provided data includes location data of the local network.2513. The method according to any of claims 10 to 12 further comprising the step of: taking an action at the server based on the consolidated data set of electricity usage.30 14. The method according to any previous claim, wherein the plurality of devicesinclude any one or more of:television;computer;refrigerator;35 freezer;14 03 25smart phone;camera;washing machine;a dryer;5 a water heater;dish washer; and / or oven.

15. The method according to any previous claim, wherein the details of the plurality of10 devices operating within the local network comprises any one or more of:device name;device type;location;IP address;15 MAC address; andbrand.

16. The method according to any previous claim, wherein the step of obtaining details of the plurality of devices operating within the local network, comprises receiving the details 20 in a standardised format.

17. The method of claim 16, wherein the standardised format is any one of: Matter; Broadband Forum, BBF; and comma separated variable, CSV.25 18. A system comprising means for carrying out the method according to any previousclaim.

19. The system of claim 18, wherein the means for carrying out the method are within a broadband router.

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